STI Publications - View Publication Form #17608

Back to Search Results Print
Publication Information
Title Charged Particle Reconstruction in CLAS12 using Machine Learning
Abstract In this work, we present studies of track parameter reconstruction from raw information in CLAS12 detector's Drift Chambers, using Machine Learning (ML). We study the resolution of tracks reconstructed with different types of ML models/algorithms, including Multi-Layer Perceptron (MLP), Extremely Randomized Trees (ERT) and Gradient Boosting Trees (GBT) using simulated data. The resulting ML model is capable of reconstructing track parameters (particle momentum, and polar and azimuthal angles) with accuracy similar to Hit Based (HB) tracking code, but $150$ times faster. Moreover, physics reactions can be identified using the particles reconstructed by the neural network in real-time (with a rate of about $34~kHz$) during experimental data collection. The developed model can be used in numerous applications, such as triggering specific physics reactions in real-time, detector performance monitoring, and real-time detector calibration.
Author(s) Gagik Gavalian, Polykarpos Thomadakis, Kevin Garner, Nikos Chrisochoides
Publication Date June 2023
Document Type Journal Article
Primary Institution Thomas Jefferson National Accelerator Facility, Newport News
Affiliation Exp Nuclear Physics / Experimental Halls / Hall B
Funding Source Nuclear Physics (NP)
Proprietary? No
This publication conveys Technical Science Results
Document Numbers
JLAB Number: JLAB-PHY-23-3757 OSTI Number: 1957503
LANL Number: Other Number: DOE/OR/23177-5728
Associated with an experiment Yes
Experiment Number(s)
E12-06-112
Associated with EIC No
Supported by Jefferson Lab LDRD Funding No
Journal Article
Journal Name Computer Physics Communication
Refereed Yes
Volume 287
Issue 1
Page(s) 108694
Attachments/Datasets/DOI Link
Document(s)
main45.pdf (STI Document)
main45.pdf (Accepted Manuscript)
DOI Link
Dataset(s) (none)
Back to Search Results Print